


import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case   							=						logging.get_logger(__name__)

__snake_case   							=						'''▁'''

__snake_case   							=						{'''vocab_file''': '''prophetnet.tokenizer'''}

__snake_case   							=						{
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

__snake_case   							=						{
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

__snake_case   							=						{
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_1_2,
}






def 		_A		(		_lowercase )					-> Optional[Any]:


     """simple docstring"""


     __UpperCamelCase =			collections.OrderedDict()
     with open(_lowercase					,  'r'					,  encoding='utf-8' ) as reader:
          __UpperCamelCase =			reader.readlines()
     for index, token in enumerate(_lowercase ):
          __UpperCamelCase =			token.rstrip('\n' )
          __UpperCamelCase =			index
     return vocab



class 	__lowerCamelCase			(_a						):
       _lowercase											=     VOCAB_FILES_NAMES
       _lowercase											=     PRETRAINED_VOCAB_FILES_MAP
       _lowercase											=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowercase											=     ["""input_ids""", """attention_mask"""]







       def __init__(			self: str,A_: int,A_: str="[SEP]",A_: List[Any]="[SEP]",A_: str="[SEP]",A_: Any="[UNK]",A_: Optional[int]="[PAD]",A_: List[str]="[CLS]",A_: Dict="[MASK]",A_: Optional[Dict[str, Any]] = None,**A_: str,):






            '''simple docstring'''




            __UpperCamelCase =			{} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                bos_token=A_,eos_token=A_,sep_token=A_,unk_token=A_,pad_token=A_,cls_token=A_,mask_token=A_,sp_model_kwargs=self.sp_model_kwargs,**A_,)

            try:
                 import sentencepiece as spm
            except ImportError:
                 logger.warning(
                     'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
                     ' pip install sentencepiece'					)
                 raise

            __UpperCamelCase =			spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(str(A_					)					)
            __UpperCamelCase =			vocab_file

            # Original fairseq vocab and spm vocab must be "aligned":
            # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
            # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
            # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
            # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

            # put special tokens and [unused] tokens into the vocab
            __UpperCamelCase =			{'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

            for i in range(10					):
                 __UpperCamelCase =			F'''[unused{i}]'''
                 __UpperCamelCase =			5 + i

            # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
            __UpperCamelCase =			12
            __UpperCamelCase =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}
            for k in self.fairseq_tokens_to_ids.keys():
                 self.unique_no_split_tokens.append(A_					)







       def __getstate__(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.__dict__.copy()
            __UpperCamelCase =			None
            return state







       def __setstate__(			self: List[Any],A_: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			d
            try:
                 import sentencepiece as spm
            except ImportError:
                 logger.warning(
                     'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
                     ' pip install sentencepiece'					)
                 raise

            # for backward compatibility
            if not hasattr(self,'sp_model_kwargs'					):
                 __UpperCamelCase =			{}

            __UpperCamelCase =			spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(self.vocab_file					)







       def 		snake_case_	(			self: Any,A_: List[int],A_: Optional[List[int]] = None,A_: bool = False					):






            '''simple docstring'''




            if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=A_,token_ids_a=A_,already_has_special_tokens=A_					)

            if token_ids_a is None:
                 return ([0] * len(A_					)) + [1]
            return ([0] * len(A_					)) + [1] + ([0] * len(A_					)) + [1]







       def 		snake_case_	(			self: Optional[int],A_: List[int],A_: Optional[List[int]] = None					):






            '''simple docstring'''




            __UpperCamelCase =			[self.sep_token_id]

            if token_ids_a is None:
                 return len(token_ids_a + sep					) * [0]
            return len(token_ids_a + sep + sep + token_ids_a + sep					) * [0]







       @property
       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            return len(self.sp_model					) + self.fairseq_offset







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			{self.convert_ids_to_tokens(A_					): i for i in range(self.vocab_size					)}
            vocab.update(self.added_tokens_encoder					)
            return vocab







       def 		snake_case_	(			self: List[Any],A_: str					):






            '''simple docstring'''




            return self.sp_model.encode(A_,out_type=A_					)







       def 		snake_case_	(			self: Any,A_: Optional[int]					):






            '''simple docstring'''




            if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
            __UpperCamelCase =			self.sp_model.PieceToId(A_					)

            # Need to return unknown token if the SP model returned 0
            return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







       def 		snake_case_	(			self: str,A_: int					):






            '''simple docstring'''




            if index in self.fairseq_ids_to_tokens:
                 return self.fairseq_ids_to_tokens[index]
            return self.sp_model.IdToPiece(index - self.fairseq_offset					)







       def 		snake_case_	(			self: Tuple,A_: int					):






            '''simple docstring'''




            __UpperCamelCase =			''.join(A_					).replace(A_,' '					).strip()
            return out_string







       def 		snake_case_	(			self: Optional[int],A_: str,A_: Optional[str] = None					):






            '''simple docstring'''




            if not os.path.isdir(A_					):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
                 return
            __UpperCamelCase =			os.path.join(
                A_,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

            if os.path.abspath(self.vocab_file					) != os.path.abspath(A_					) and os.path.isfile(self.vocab_file					):
                 copyfile(self.vocab_file,A_					)
            elif not os.path.isfile(self.vocab_file					):
                 with open(A_,'wb'					) as fi:
                      __UpperCamelCase =			self.sp_model.serialized_model_proto()
                      fi.write(A_					)

            return (out_vocab_file,)







       def 		snake_case_	(			self: Tuple,A_: List[int],A_: Optional[List[int]] = None					):






            '''simple docstring'''




            if token_ids_a is None:
                 return token_ids_a + [self.sep_token_id]
            __UpperCamelCase =			[self.sep_token_id]
            return token_ids_a + sep + token_ids_a + sep







'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


SCREAMING_SNAKE_CASE__     :						List[str]	=  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE__     :						Dict	=  {
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE__     :						Optional[int]	=  {
    '''facebook/bart-base''': 1_0_2_4,
    '''facebook/bart-large''': 1_0_2_4,
    '''facebook/bart-large-mnli''': 1_0_2_4,
    '''facebook/bart-large-cnn''': 1_0_2_4,
    '''facebook/bart-large-xsum''': 1_0_2_4,
    '''yjernite/bart_eli5''': 1_0_2_4,
}

class 							a__( snake_case__     ):
			a_	: Dict									=      VOCAB_FILES_NAMES
			a_	: Tuple									=      PRETRAINED_VOCAB_FILES_MAP
			a_	: Tuple									=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_	: List[Any]									=      ['''input_ids''', '''attention_mask''']
			a_	: Union[str, Any]									=      BartTokenizer






			def __init__(     self					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase="replace"					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="<unk>"					,   _UpperCAmelCase="<pad>"					,   _UpperCAmelCase="<mask>"					,   _UpperCAmelCase=False					,   _UpperCAmelCase=True					,   **_UpperCAmelCase					,   )							->							int:
					super().__init__(
					    _UpperCAmelCase					,   _UpperCAmelCase					,   tokenizer_file=_UpperCAmelCase					,   errors=_UpperCAmelCase					,   bos_token=_UpperCAmelCase					,   eos_token=_UpperCAmelCase					,   sep_token=_UpperCAmelCase					,   cls_token=_UpperCAmelCase					,   unk_token=_UpperCAmelCase					,   pad_token=_UpperCAmelCase					,   mask_token=_UpperCAmelCase					,   add_prefix_space=_UpperCAmelCase					,   trim_offsets=_UpperCAmelCase					,   **_UpperCAmelCase					,   )

					snake_case__	     =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
					if pre_tok_state.get('add_prefix_space'					,   _UpperCAmelCase						) != add_prefix_space:
							snake_case__	     =getattr(_UpperCAmelCase					,   pre_tok_state.pop('type'						)						)
							snake_case__	     =add_prefix_space
							snake_case__	     =pre_tok_class(**_UpperCAmelCase						)

					snake_case__	     =add_prefix_space

					# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
					snake_case__	     ='post_processor'
					snake_case__	     =getattr(self.backend_tokenizer					,   _UpperCAmelCase					,   _UpperCAmelCase						)
					if tokenizer_component_instance:
							snake_case__	     =json.loads(tokenizer_component_instance.__getstate__()						)

							# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
							if "sep" in state:
									snake_case__	     =tuple(state['sep']						)
							if "cls" in state:
									snake_case__	     =tuple(state['cls']						)

							snake_case__	     =False

							if state.get('add_prefix_space'					,   _UpperCAmelCase						) != add_prefix_space:
									snake_case__	     =add_prefix_space
									snake_case__	     =True

							if state.get('trim_offsets'					,   _UpperCAmelCase						) != trim_offsets:
									snake_case__	     =trim_offsets
									snake_case__	     =True

							if changes_to_apply:
									snake_case__	     =getattr(_UpperCAmelCase					,   state.pop('type'						)						)
									snake_case__	     =component_class(**_UpperCAmelCase						)
									setattr(self.backend_tokenizer					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			@property
			def 						_lowercase  (     self						)							->							str:
					if self._mask_token is None:
							if self.verbose:
									logger.error('Using mask_token, but it is not set yet.'						)
							return None
					return str(self._mask_token						)






			@mask_token.setter
			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							int:
					snake_case__	     =AddedToken(_UpperCAmelCase					,   lstrip=_UpperCAmelCase					,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						) else value
					snake_case__	     =value






			def 						_lowercase  (     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							BatchEncoding:
					snake_case__	     =kwargs.get('is_split_into_words'					,   _UpperCAmelCase						)

					if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    'to use it with pretokenized inputs.'						)

					return super()._batch_encode_plus(*_UpperCAmelCase					,   **_UpperCAmelCase						)






			def 						_lowercase  (     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							BatchEncoding:
					snake_case__	     =kwargs.get('is_split_into_words'					,   _UpperCAmelCase						)

					if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    'to use it with pretokenized inputs.'						)

					return super()._encode_plus(*_UpperCAmelCase					,   **_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							Tuple[str]:
					snake_case__	     =self._tokenizer.model.save(_UpperCAmelCase					,   name=_UpperCAmelCase						)
					return tuple(_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase=None						)							->							Optional[int]:
					snake_case__	     =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
					if token_ids_a is None:
							return output

					return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							List[int]:
					snake_case__	     =[self.sep_token_id]
					snake_case__	     =[self.cls_token_id]

					if token_ids_a is None:
							return len(cls + token_ids_a + sep						) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]




